import gym
import torch
import torch.nn.functional as F
import numpy as np
import matplotlib.pyplot as plt
import rl_utils
from tqdm import tqdm


class PolicyNet(torch.nn.Module):
    def __init__(self, state_dim, hidden_dim, action_dim):
        super(PolicyNet, self).__init__()
        self.fc1 = torch.nn.Linear(state_dim, hidden_dim)
        self.fc2 = torch.nn.Linear(hidden_dim, action_dim)

    def forward(self, x):
        x = F.relu(self.fc1(x))
        return F.softmax(self.fc2(x), dim=1)        #dim指定softmax维度


class ValueNet(torch.nn.Module):
    def __init__(self, state_dim, hidden_dim):
        super(ValueNet, self).__init__()
        self.fc1 = torch.nn.Linear(state_dim, hidden_dim)
        self.fc2 = torch.nn.Linear(hidden_dim, 1)

    def forward(self, x):
        x = F.relu(self.fc1(x))
        return self.fc2(x)


class PPO:
    ''' PPO算法,采用截断方式 '''
    def __init__(self, state_dim, hidden_dim, action_dim, actor_lr, critic_lr,
                 lmbda, epochs, eps, gamma, device):
        self.actor = PolicyNet(state_dim, hidden_dim, action_dim).to(device)        #指定每个动作的概率
        self.critic = ValueNet(state_dim, hidden_dim).to(device)                    #指定价值
        self.actor_optimizer = torch.optim.Adam(self.actor.parameters(),lr=actor_lr)
        self.critic_optimizer = torch.optim.Adam(self.critic.parameters(), lr=critic_lr)
        self.gamma = gamma
        self.lmbda = lmbda
        self.epochs = epochs  # 一条序列的数据用来训练轮数
        self.eps = eps  # PPO中截断范围的参数
        self.device = device

    def take_action(self, state):
        state = torch.tensor([state], dtype=torch.float).to(self.device)
        probs = self.actor(state)
        action_dist = torch.distributions.Categorical(probs)
        action = action_dist.sample()
        return action.item()

    def update(self, transition_dict):
        states = torch.tensor(transition_dict['states'],dtype=torch.float).to(self.device)
        actions = torch.tensor(transition_dict['actions']).view(-1, 1).to(self.device)
        rewards = torch.tensor(transition_dict['rewards'], dtype=torch.float).view(-1, 1).to(self.device)
        next_states = torch.tensor(transition_dict['next_states'], dtype=torch.float).to(self.device)
        dones = torch.tensor(transition_dict['dones'],dtype=torch.float).view(-1, 1).to(self.device)
        td_target = rewards + self.gamma * self.critic(next_states) * (1 -dones)
        td_delta = td_target - self.critic(states)      #td error


        advantage = rl_utils.compute_advantage(self.gamma, self.lmbda,td_delta.cpu()).to(self.device)
        old_log_probs = torch.log(self.actor(states).gather(1,actions)).detach()

        for _ in range(self.epochs):
            log_probs = torch.log(self.actor(states).gather(1, actions))
            ratio = torch.exp(log_probs - old_log_probs)
            surr1 = ratio * advantage
            surr2 = torch.clamp(ratio, 1 - self.eps,1 + self.eps) * advantage  # 截断
            actor_loss = torch.mean(-surr2)  # PPO损失函数
            critic_loss = torch.mean(F.mse_loss(self.critic(states), td_target.detach()))
            self.actor_optimizer.zero_grad()
            self.critic_optimizer.zero_grad()
            actor_loss.backward()
            critic_loss.backward()
            self.actor_optimizer.step()
            self.critic_optimizer.step()

actor_lr = 1e-3
critic_lr = 1e-2
num_episodes = 500
hidden_dim = 128
gamma = 0.98
lmbda = 0.95
epochs = 10
eps = 0.2
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")

env_name = 'CartPole-v1'
env = gym.make(env_name)
torch.manual_seed(0)
state_dim = env.observation_space.shape[0]
action_dim = env.action_space.n
agent = PPO(state_dim, hidden_dim, action_dim, actor_lr, critic_lr, lmbda, epochs, eps, gamma, device)

return_list = []
for i in range(10):
    with tqdm(total=int(num_episodes/10), desc='Iteration %d' % i) as pbar:
        for i_episode in range(int(num_episodes/10)):
            episode_return = 0
            transition_dict = {'states': [], 'actions': [], 'next_states': [], 'rewards': [], 'dones': []}
            state = env.reset(seed=0)[0]
            done = False
            j = 0
            while not done:
                j += 1
                action = agent.take_action(state)
                next_state, reward, done, _,_ = env.step(action)
                if j == 500:
                    done = True
                transition_dict['states'].append(state)
                transition_dict['actions'].append(action)
                transition_dict['next_states'].append(next_state)
                transition_dict['rewards'].append(reward)
                transition_dict['dones'].append(done)
                state = next_state
                episode_return += reward
            return_list.append(episode_return)
            agent.update(transition_dict)
            if (i_episode+1) % 10 == 0:
                pbar.set_postfix({'episode': '%d' % (num_episodes/10 * i + i_episode+1), 'return': '%.3f' % np.mean(return_list[-10:])})
            pbar.update(1)